What Is Product Analytics: Definition, Metrics, Tools and Examples

What is Product Analytics?

Product analytics is the study of how customers interact with a product or service. Product analytics is used by companies to analyze and visualize user experiences. This allows teams to optimize features for their users based on feedback and user data, rather than relying on intuition and gut feelings.

Importance of Product Analytics

Before you launch a new product or feature, Companies often rely on customer surveys and interviews to collect data about potential customers. Learn about the challenges and validate your assumptions.

After launching, the company must adapt its product strategy to use behavioral data and hard evidence instead. How their users interact with the product.

Validate the hypothesis that was used to design the product by using real behavior data. Although prospects might have expressed an interest in the product through a wishlist form, they may not actually use the features that are intended to satisfy their wishlist.

Businesses need to use real product data to analyze product usage and customer experience.

Product Analytics Metrics and Examples

Product analytics can help answer many questions, including trends and analyzing feature adoption over time. It also allows you to visualize complex experiences and user flows within the product.

These are the five most critical types of analysis that you need to use in order to get the best out of product analytics.

1. Trend Analysis

Product Analytics is a popular way to analyze trends over time. This helps companies see whether feature adoption is growing or declining over time. The Trends Analysis focuses on one or more touchpoints of the user journey, cutting and dicing them, and zooming in on their performance over time.

UX Designers have the ability to zoom in on specific features and see how user configuration changes change over time.

Product teams can determine which features are most frequently used and how they compare to previous versions.

2. Journey Analysis

A user must follow a series of steps in order to solve most product-related problems. These use cases may occur over a prolonged period of time and involve multiple users and multichannel interactions (Macro Journey). Other uses can be as simple as clicking a few buttons. You can submit a form (Micro Journey) or configure an account.

It is important to imagine the user’s journey to achieve every goal, and where they might be stopping along the way.

To further refine the user experience, the Journey Analysis gives better visibility to bottlenecks along the user journey.

Also read: Product Growth Strategies: Definition, Types, and Examples

3. Attribution Analysis

Customers who have achieved success can provide valuable insight. The Attribution Analysis will help you pinpoint the key touchpoints that contribute to success. Similar to the journey analysis, the attribute analysis uses user flow data. Instead, it focuses on users who have completed their journeys and analyzes their touches in reverse.

For example, a Product Manager could break down the revenue from premium feature usage before conversion. This will enable companies to assign a dollar value for each premium feature and highlight top converting opportunities.

4. Cohort Analysis

As a company grows, so will its product messaging, website design and documentation, onboarding experience, brand awareness, and product complexity. These changes will have a profound impact on how each cohort views the products or services. To show how perceptions change over time, it is important to break down product metrics by different cohorts.

A younger cohort that started using the product earlier may be more successful than a less experienced cohort. The product will not become more complex for the new cohort as it did for the older.

A younger cohort that has had a positive customer experience can have a better foundation than an older group that may not have had proper onboarding.

You can also define cohorts based on many factors. You can define a cohort based on when they first visited the website, signed up for the service, talked to a salesperson, or upgraded to a paid membership. Each cohort analysis should have a unique and dynamic definition of a cohort.

5. Retention Analysis

The majority of successful businesses experience some degree of churn. The rate at which churn is occurring must be analyzed by companies. The Retention Analy allows you to analyze how users interact with a product over multiple periods. A Retention Analysis is different from Cohort Analysis. It normalizes cohorts within the Initial period and displays an aggregate retention rate for each of the subsequent periods.

Each use case will have a different set of cohort units. They can be a few days, weeks, or months. Sometimes, they even span quarters.

Every Retention Analysis is similar to Cohort Analysis. It requires a cohort description (an action that defines the group, like signing up), and a repeat (an action that can be analyzed over time, such as logging in).

Who Should Use A Product Analytics Tool?

Multiple teams can benefit from the insights provided by product analytics platforms. This would be especially beneficial to these teams.

Product Team

Product managers are the most prominent. Analytics data can reveal a lot about users’ behavior. Analytics is a great place to find out about customer behavior and inform product decisions.

Here are some examples of data points the product team could gain:

• Which features do customers spend the most time with?
• Customer engagement metrics such as where they click, watch, read, and do business within the product
• You can identify places that cause friction by monitoring bounce rates and pages with on-page times
• Customer behavior patterns that predate product upgrades or downgrades
• Product marketing, product analysts, and product leaders can all benefit from the actionable information that will help inform product decisions.

Marketing Team

Their product analytics software can provide valuable insights beyond marketing.

These could be:

• Measurement of conversion rates for in-product marketing offers
• How page load times, design, and other UX factors impact in-product sales
• Different marketing copy and CTA placements tested
• To find out which features customers are most interested in, we can use this information to help with our external marketing efforts
• Tracking the success of each campaign and marketing channel is possible only if you have full visibility of their funnel. * Incomplete visibility of marketing channels can lead to blind attribution efforts.
• Facebook advertising might be responsible for the highest number of account creations. However, the email marketing channel could be the one driving the greatest in-app sales.

Which channel is credited for the sale? No one can know without proper attribution, which includes product analytics.

Other Team Members

A product analytics platform can be a great tool for the UX designer, dev team leaders, data scientists, and everyone else involved in improving a product’s performance or creating better products.

Many digital products are managed by companies that have some form of platform.

Also read: AI in Product Development: What are the Role and Benefits

How to Implement Product Analytics

It is possible to implement product analytics easily. To collect useful data, companies must establish a common discipline. The development and deployment processes are streamlined when a discipline is clearly defined.

This discipline includes:

• What are the user actions?
• How to collect user actions
• When should user actions be collected?

What are User Actions?

Three parts make up user actions:

• Action Name: The action the user is performing. It is usually defined as a verb, followed by a noun. You can view the tab, invite users, submit an application, create a project, or update your profile.
• Action properties: Each action will have its own set (or taxonomies) of properties. For example, the view tab can have properties such as name and URL (URL for the tab).
• Action timestamp: This is the time that this action took place, typically stored in Unix Timestamp format.

Developers won’t have to spend time trying to guess the correct action name or properties for each new interaction or feature once this action definition discipline has been documented.

How to Collect User Actions

App interactions will be tracked using Javascript (for the web), Android SDK (for native apps), or iOS SDK (for mobile). These SDKs are often used by developers in their development frameworks. Developers are encouraged to abstract the implementation to maintain consistent tracking.

React developers can, for example, plug a view screen action into the React Router. This will track the action every time the screen is switched. Developers don’t have to instrument that action each time they add a new screen to their app.

This engineering discipline will prevent double-tracking actions and track the same action type under different names. It is especially useful when multiple developers are involved. This engineering discipline will almost automate the tracking process for new features.

When to Collect User Activities

The resolution of the data the company wants to track will determine the frequency of the activity tracking. Many companies track every interaction, including click, scrolling, and hovering the mouse. Others might limit their tracking to less prominent actions.

Although more actions can provide a better user experience, data collection costs can be exponentially higher. However, too much data collection might miss important issues in the user experience such as users not clicking a button.

Below are the various layers of tracking resolutions that companies should be aware of, sorted by importance

• Sessions (must be): This resolution should include account creation, loading of the app, and logging in. This will suffice to provide high-level growth analysis and user retention analysis.
• CRUD Operations (highly recommended): This resolution deals with when a user creates or reads, updates, deletes, or modifies any product entity.
• User Selections: This resolution describes how users interact with a particular feature, such as selecting from a dropdown or switching accessibility modes.
• User Interactions: This resolution includes all possible interactions such as mouse clicks and hovers or expanding a dropdown. These actions can be device-specific, and may not have an impact on the macro user journey.

Product Analytics implementation will be easier if there is a clear and consistent guideline for when to track and what not. This will ensure a consistent resolution throughout the entire user journey.

Also read: Product Development Process | Definition and Overview

Which Product Analytics Tools Should I Use?

Companies will need to choose the right tools after they have defined the data disciplines that will be used in their product analytics project.

Google Analytics and GA4 aren’t always robust enough to meet your basic needs.

There are many great tools available. Companies must choose one of the two options for implementing product analytics before they can select the right tools.

The first is to create an internal solution, which includes A Data Warehouse as well as a Business Intelligence tool. The second is to use a dedicated product analysis service.

Data Warehouse and Business Intelligence Tool

This approach has a few advantages:

• Data architecture will be managed by companies. This requires Data Engineering expertise, which is usually a full-time job.
• Data governance and compliance will be in the full hands of companies. They determine what data they will expose.
• The report’s content and presentation will be controlled by the company.

However, there are also many downsides.

• This project requires the involvement of multiple experts.
• SQL is used to query data warehouses. SQL is not the best query engine for user flows. To answer a simple question about attribution, could require over 200 lines of SQL code.
• This method can be more costly. Companies will still require a data collection solution, data warehouse, and business intelligence solutions, in addition to the human resources needed to implement this approach.
• This method can take several months to put into practice.

This is a good option for larger companies that want to use data for multiple purposes, including analytics for customers. They will be able to create embedded analytics systems for customers and build their internal dashboards using the same source as the customer’s data.

Product Analytics Solution

A cloud-based Product Analytics solution offers many benefits:

• Zero maintenance solution. Companies don’t have to worry about data architecture scaling.
• The visual interface helps answer product analytics questions that involve user flows. These types of queries are not answered by SQL query engines. Instead, Product Analytics query engines can be tailored for these kinds of problems.
• Democratizes data between all teams. The interface is easy to use and doesn’t require SQL knowledge Technical expertise is required to answer the most complex questions.
• More affordable. Does not require infrastructure provisioning.

The problem with using off-the-shelf product analysis software is that each solution has its own approach to creating reports. This can make it difficult for an organization to visualize data analytics in a particular way.

This can prove to be a benefit for many organizations. These companies can draw on the experience of those who have worked for years to design these products, and then they can focus their efforts on providing customer service.

It is crucial to develop a data strategy together with your team. This strategy will impact the way your company implements Product Analytics.